Rating a physical capability by motion analysis

ABSTRACT

Motion Analysis is used to classify or rate human capability in a physical domain via a minimized movement and data collection protocol optionally producing a discreet, overall figure of merit of the selected physical capability. The minimal protocol can be determined by data mining or other analysis of a more extensive movement and data collection. Protocols can be relevant in medical, sports and occupational applications. Kinematic, kinetic, body type, Electromyography (EMG), Ground Reactive Force (GRF), demographic, and psychological data can be encompassed. Resulting protocols can be capable of transforming raw data representing specific human motions into an objective rating of a skill or capability related to those motions.

RELATED APPLICATIONS

This application is a continuation application of application Ser. No.13/369,145 filed Feb. 8, 2012, which is a continuation application ofU.S. non-provisional application Ser. No. 12/792,088 filed Jun. 2, 2010.That application, in turn, claims the benefit of U.S. provisionalapplication 61/238,039 filed Aug. 28, 2009, and of U.S. provisionalapplication 61/328,614 filed Apr. 27, 2010. All the foregoingapplications are hereby incorporated herein by reference in theirentirety.

FIELD

These teachings relate to the analysis of human movements in order toassign a classification or rating to a physical capability or conditionrelated to those movements.

BACKGROUND

Kinematic and kinetic measurements have been made for the purpose ofunderstanding human physiology, for diagnosing disorders, for sportsstudy, and for sport performance improvement. Movement data has beencollected by a variety of measurement techniques including by devicesattached to the body, and by cameras detecting movement of body parts,and by detecting movement of specially marked points on a body.

Specialized sports, clinical, and research use of this technology haveincluded the coaching of elite athletes, predicting the later appearanceof Cerebral Palsy symptoms in infants, and tracking improvements over acourse of treatment. Costs of dynamic body motion and force measurementdevices have lowered and biomechanical knowledge has increased. However,the wide array and complexity of possible human motions, the largeamount of raw data generated, and particularly a lack of results thatare useful without expert interpretation, have significantly limited theroutine exploitation of the tools and techniques of this field.Inexpensive and routinized solutions to incorporate motion-basedmeasurements into everyday health care can have a great importance inoverall cost control.

SUMMARY

There are sources of movement data collection that can provide volumesof information from instrumented movements. The teachings herein canmake biomechanical data relevant to clinicians and coaches by producingand using protocols that can provide understandable ratings relevant toa physical task of interest. Motion test protocols can advantageously beadministrable by modestly trained individuals and provide resultsrapidly and preferably relatively automatically.

Methods and systems taught herein can include an ordinal or cardinalscalar rating or an objectively defined discreet classification. Theseratings and classifications can be of a physical capability or physicalperformance based on measurements made during performance of prescribedmovement protocol. A set of predetermined, relevant movement-relatedinformation can be collected for analysis. The collected information canbe analyzed in light of predetermined criteria to produce an objectiveclassification or rating. Various applications of these teachings canhave distinct executable movement and measurement protocols. Data miningtechniques can be used over data representation of a large group ofindividuals to identify key parameters of movements to allow unknownsubject's to be classified. Methods and systems for performing tests andproducing a quantified rating of subjects, as well as methods andsystems for creating such protocols, are within the teaching herein.

BRIEF DESCRIPTION OF DRAWINGS

The various drawings are to better illustrate the concepts describedherein and to better teach those skilled in the art to make, use, andcarry out these teachings. They are not intended to be limiting or toset metes and bounds.

FIG. 1A shows a human in a schematic view and indicates examples of somemotions that might be included in developing a protocol;

FIG. 1B shows a human in a schematic view and a further example of amotion that might be included in developing a protocol;

FIG. 2A illustrates one possibility for instrumentation of a human'smotion involving a body suit with embedded motion and direction sensors;

FIG. 2B illustrates one possibility for instrumentation of a human'smotion using visual markers in various body locations viewed by multiplecameras for 3D position determination;

FIG. 3 is a flow chart of the data collection steps involved with anexample embodiment of protocol creation;

FIG. 4 is a flow chart of the data mining and analysis steps involvedwith an example embodiment of protocol creation;

FIG. 5 is a flow chart of the data collection steps involved with anexample of protocol execution;

FIG. 6 is a flow chart of the data mining and analysis steps involvedwith an example embodiment of protocol creation;

FIG. 7 illustrates a system for performing a protocol for evaluating atennis swing; shown are a human subject with attached accelerometers atselected locations, a data capturing and preprocessing computer, and adata analysis computer.

DETAILED DESCRIPTION

Introduction

Methods and Systems for developing and using evaluative test protocolsare described by way of example embodiments. By protocol, as usedherein, generally mean (a) a preplanned set of steps or actions togather particular information related to a subject, student or patient;(b) a preplanned series of steps and actions to analyze, scale, compare,or transform that “raw” information and; (c) a predetermined method andcriteria for assigning a rating, score, index or labeled classificationbased upon the information analysis. While a full protocol would includethe steps of (a), (b), and (c) above, the term protocol can signifythese steps individually. A trivial example of a protocol would be thesteps for taking someone's blood pressure. A more complex protocol mightbe the series of steps involved in preparing a patient, configuringequipment, and administering an MRI scan.

Data mining includes mathematical and computational techniques ofunstructured analysis and correlation between multiple parameters. Thesetechniques help to uncover unexpected relationships between theparameters. When the data forms in relatively tight groupings thosegroupings can be called clusters.

The examples presented include both methods for developing a specificprotocol and the performance of those executable protocols to transformmotion data representative of an individual's performance into a readilyunderstood classification or figure of merit.

EXAMPLES

The many areas of relevant human physical capability assessment include,for example: sports training, occupational choices, geriatricassessments, medical treatment progression, medical diagnostics, andmalingering detection. Other areas of human physical activity relevantto the teachings herein include physical therapy, exercise routines, andgame playing. Despite the availability of low-cost motion and forcesensors and a rich understanding of biomechanics, assessments arenonetheless routinely made in a subjective manner or by staticmeasurements such as range-of-motion. Alternatively, there are motionlabs that can produce volumes of real-time raw motion information fromwhich it may be difficult to draw conclusions. Some automatic andsemi-automatic methods and systems applying these teachings have thepotential to significantly lower a wide set of health care costs byadding quantized motion-related measurements to everyday medical care.

Examples of the teachings herein can use data mining techniques tosemi-automatically analyze a set of collected movement and non-movementrelated parameters. That analysis can determine the statisticalsignificance of each member of the set of parameters, in regards to acorrelated attribute. A subset of more significant tests from theoriginal comprehensive set of information can be identified forincorporation into an efficient executable protocol on a Paretoprinciple basis.

Method Overview

Embodiments of protocols for objective, repeatable, and quantifiedratings based on kinematic, kinetic and other data can be developed by:

-   (1) Applying subject domain knowledge to postulate a comprehensive    universe of movements and a comprehensive universe of collectable    data to be representative of parameters of those movements.-   (2) For a set of subjects with known attributes in the particular    domain of interest: directing subjects to perform the predetermined    universe of movements while instrumented to collect data regarding    the universe of parameters.-   (3) Analyzing the collected data with linear and non-linear    mathematical and computational methods. Those methods can include:    multivariate regression, neural networks, and data mining by    classification, clustering, self-organizing maps, and other    approaches. The goal of the analysis can be to find parameters that    correlate with the subjects' pre-known capabilities.-   (4) Organizing the parameters by their predictive power or    correlative strength.-   (5) Building up a list of movements and their respective parameters    to produce an executable protocol from a subset of the original    universe of movements. The subset collectively having a desired    level of overall predictive and correlative power.

Protocols consistent with the principles herein can also involvemeasuring non-motion parameters such as EMG (Electromyogram) and GroundReactive Force (GRF) information and can also involve static variablessuch as body type, demographic, physiological, static biomechanicalfactors, and psychological information.

Example 1 Back Pain Assessment

In the case of back pain assessment, a set of subjects with known,varying degrees of impairment are tested.

With some clinical insight, a comprehensive set of motions areprescribed for subjects to perform while a comprehensive set ofparameters characterizing those movements is collected. FIGS. 1A and 1Billustrate a person 1 and a variety of possible prescribed motions. Forexample, subjects may be instructed to bend in one or more specificdirections 2, to stand from a sitting position 3, to twist body portions4 at various rates, to walk normally, and turn or move various bodyparts 5 6. Motions might be repeated multiple times. Prescribed motionsmight comprise motions that are performed under load and those that arenot loaded or with a different degree of load. Both motions that involvebiomechanically open kinetic chains and those that involve closedkinetic chains might be used.

Those skilled in the art will recognize that there are many ways tocollect human movement related parameters. FIGS. 2A and 2B illustratealternate methods of 3D real-time full-body instrumentation. FIG. 2Aschematically represents a person wearing a full body suit 10 and pointsout locations of embedded motion and direction sensors 11 thatcommunicate with a data analysis computer 12. FIG. 2B, in contrast,shows a person with a plurality of marked spots 13. A multi-camerasystem 14 provides an apparatus to track the location of each spot inreal time via a 3D tracking system 15. Some versions use passive spotsand others can use actively light emitting spots. The tracked spot dataand data from a pressure plate 16 are analyzed by a data analysiscomputer 17. In addition to positions in space, other parameters such aschanges in joint angles and EMG data could be determined as well.

Each of the subjects with known capabilities or known impairmentsperforms the determined set of motions while instrumented. Theinformation is accepted and stored. The large volume of informationresulting from the above tests is analyzed with non-linear techniquesincluding artificial neural nets (ANN), self-organizing maps (SOM),machine learning classification trees, fuzzy classification, and otherdata mining techniques. Analysis by regression, multivariate analysis,and other more traditional statistical methods may be employed.

These analyses can produce a clustering of the various subjects'performance into discreet classifications or can find correlativestatistical significance between the parameters and the knowncategorization of capabilities of the various subjects. An additionalstep is to then produce a subset of the initial motions and initialparameters that are particularly sensitive and have statisticallysignificant power in indicating a classification membership or a rating.As those familiar with the art will understand, this is accomplished byfurther statistical analysis to identify the motions and the parametersassociated with those motions that have the greatest predictive power inassociating an individual with a cluster, a classification, or a rating.Starting from the most sensitive motion and related parameters on down,a list of motions and measurements is compiled for potential addition toan executable protocol until a desired balance between ease of protocoladministration and statistical reliability is achieved. That list ofmotions and their associated salient parameters become the basis for anexecutable protocol. Rather than strictly using the top ranked motionsand parameters as the basis of an executable protocol, tradeoffs betweenpredictive power and ease of performing the various movements andmeasuring the various parameters may be made as well. A protocol can bedevised that makes trade-offs between time to administer, cost, andcomplexity of instrumentation, versus confidence in a test'sconclusions.

In the case of back pain the subjects have a range of back pain of knownseverity including some with no back pain. That information is comparedto the collected movement parameters. The resulting protocol is intendedto produce an overall measure of back impairment or back health thatmight be used to objectively assess progress over a course of therapy.For back pain assessment or for general back performance capability, ascalar index of 1-10 can apply.

Example 2 Malingering Assessment

A second application example, also related to back pain, is a protocolfor detection of malingering or “sincerity of effort”. Rather thanresult in a scalar index of back health this protocol produces atwo-state classification of insincere/sincere effort orfaking/not-faking within stated confidence levels. Following theteaching herein, a range of possible movements and measures of thosemovements were postulated and information regarding those particularfactors was measured and analyzed for both actual back pain sufferersand for control subjects. One hypothesis of this assessment was thatchronic back pain would result in a fairly consistent motioncharacteristic as the “point of pain” was entered in performing aprescribed movement. In other words, if a subject was asked to perform atask that resulted in back pain, he or she would experience it at thesame point in the movement each time a task was performed. Furthermoreit was also hypothesized that the subject might begin to slow down atthe point of pain and then be able to accelerate again after the paindiminishes. Subjects were instrumented using a Lumbar Motion Monitor (anexoskeleton attached to the back that can measure range of motion,velocity and acceleration in all three planes of motion). Severalmovement tasks requiring forward flexing and then extension and finallyreturning to the starting position were devised. This cycle was dividedinto 360-degrees so that each trial could be time-normalized to othertrials on a position by-position basis. Using the LMM, it wasestablished that movement information could be captured and storedincluding peak acceleration, average acceleration, peak velocity,average velocity as well as consistency. Other variables such as height,weight, length of limbs, position of foot, anthropometrical details, andother biomechanical factors of each individual were added to thecollected data.

Two groups of subjects were tested. One was a group of 19 patients withchronic back pain. This fact was established both by history and by aphysical performed by a physician. The second group of 20 had no historyof back pain. Both groups were asked to perform the predeterminedmovement protocol tasks as best they could with full effort. Each groupwas then asked to repeat the same movement tasks, this time “pretending”they had back pain at a specific location in an attempt to convince usthat they had real pain at that location. The goal was to find a groupof variables that are readily measurable and, taken together, canreliably place an unknown individual into the correct group.

Over 100 movement and static variables for each subject were derivedfrom the measurements. A statistical regression analysis, consistentwith the teachings herein, was performed to see if any subset of theseparameters, in combination, had enough predictive power to result inclustering of data that reliably placed a subject into the correct group(faking or non-faking). The analysis produced a formula with a 91percent chance of placing an unknown individual into the proper group.The most salient factors were related to abruptness of change inacceleration near the point of pain and the consistency of that measure.The “fakers” did not produce the acceleration/deceleration profile atthe point of “pain” to a degree and with consistency as to location andas to timing when compared to those with actual back disorders.

An executable protocol was developed to particularly instrument,compute, and evaluate the acceleration/deceleration profile at the pointof inflection. This protocol could quickly and reliably categorizesincere and insincere self-reporting of back pain.

Example 3 Golf Performance Index

A sport performance example consistent with the principles taught hereinis a “Golf Performance Index”. GPI score is a scalar rating of overalllevel of performance in a golf skill. One way to think about this is asa process for transforming data comprising a time-series of valuesrepresenting human motions into an objective meaningful measureproviding that person's golf swing rating. While learning a new swing asubject may be progressing steadily in their mastery of that new skillbut in fact be producing erratic end-results. To coach or to self-coach,an objective measure of progress in learning that swing other than byultimate outcomes can be valuable. Determining an overall figure ofmerit of a swing execution based on minimal measurements (for costreasons and to reduce the intrusive instrumentation borne by the golfer)is desired. A figure of merit or rating achievement of a desired swingcan give more valuable feedback to a student than the final outcome ofball flight or golf score. These final outcomes are unduly affected byvery small differences in execution or in external factors. Initially acomprehensive set of parameters that may contribute to the accuracy of agolf swing's result is postulated. Motion information is collected overa set of golfers representing a wide range of abilities. Also ballflight accuracy is measured. Those skilled in the art will understandthat this can be accomplished either by golfers actually hitting ballsor by a virtual golf simulation.

Correlations are determined between each motion parameter, combinationsof motion parameters, and ball flight. This can be accomplished byclassical linear regression techniques or by data mining techniquesincluding clustering. As in other examples, this mathematical analysiscan rank the various motions and measurements of the comprehensive setby their respective statistical predictive power. Keeping practicalityof measurement in mind, a subset of motion parameters with effectivepredictive power is listed and forms the framework of an executableprotocol. The number of parameters from the initial set that end up inthe executable protocol is based on a desired degree of statisticalconfidence.

Example 4 Occupational Assessment

Rather than describe this example in detail, below is presented aproblem amenable to attack by the methods taught herein.

An objective measurement of the capability of making particularjob-related movements, particularly under load, is valuable in assessingworkers. Periodically, employees can be tested as part of a program topromote safety and health as well as to assess the job-readiness ofemployees recovering from injury. In cases of recovering fromdisability, for example, an employee may be deemed ready to return towork when they have regained their pre-injury level in the relevantphysical capability. Being able to objectively measure the employees ina job category and then know what level a particular person should berestored to before returning to work would save money and time.

Example Method of Creating Protocols

A method for developing an executable protocol includes the steps:

-   1) Accepting a specific domain of interest.-   2) Selecting a comprehensive set of real-time motion-related    parameters relevant to the domain of interest and selecting other    variables to be measured or surveyed.-   3) Accepting and storing data from multiple runs of performance with    various subjects while those subjects perform the comprehensive set    of movements while the comprehensive set of parameters are    collected.-   4) Analyzing resulting data by at least one of the following    techniques: (a) non-linear data mining techniques to find    classification clusters, decision tree classifiers, or ordinal, or    scalar, or vector rating; and (b) classical statistical methods to    determine correlations and other statistically valid relationships    to allow meaningful classification or scalar or ordinal indices to    be discovered.-   5) Ranking the movements and the parameters of those movements by    their statistical predictive power.-   6) Selecting a subset of the comprehensive movements based on their    ranking to comprise the movements and parameters of an executable    protocol having a desired level of statistical reliability in    classification or rating.

Protocol Method

Example method for administering a protocol by the steps of:

-   1) Instrumenting subject for predetermined, real-time motion-related    measurements.-   2) Performing, by a subject, a predetermined motion sequence.    Collecting data regarding a predetermined set of parameters;-   3) At least a subset of the collected motion information is    formatted for computerized statistical analysis to produce a result    that is (1) a classification or (2) an ordinal or (3) scalar rating.-   4) Optionally permit real-time viewing of protocol performance by a    remotely located monitor.-   5) Optionally store video documenting protocol performance along    with the data for later verification of correct protocol    administration.

The data analyzing step can be performed at a different location andtime than the actual testing of the subject.

Comprehensive Parameters

Those with subject matter knowledge in the application domain ofinterest may advantageously postulate an initial comprehensive set offactors. Preferably motions, measurements, and derived parameters to be“in the mix” of initial widely constituted measures can include: higherorder quantities such as velocity and acceleration, consistency ofperformance in repeated motions, and angle/angle comparisons of pairs ofcoordinating body structures. Both motions constituting open kineticchains and those constituting closed kinetic chains and those unloadedand loaded are also preferably in the initial comprehensive set ofparameters. Spectrally pre-processed data, in addition to time-domaindata may bring correlative relationships to light. In some cases it ispreferred to also include body type, demographic, and psychologicalvariables.

Programmed Computer Systems

For analyzing of measured and calculated parameters, data miningworkbench software such as Weka, Orange, Matlab, IBM DB2 IntelligentMiner and statistical software tools such as SPSS can be used.

During Protocol Creation

Example method steps performed on data recording computer systems:

-   1) Filtering and normalize data.-   2) Computing predetermined parameters from raw data (velocity    determined from acceleration or kinetic information from kinematic    information, for example).-   3) Packaging data for use by an analysis system.

Data Analysis Computational Techniques—Protocol Development

One goal of some embodiments consistent with the teachings herein is todiscern a subset of motions and subset of possible data measures ofthose motions to be automatically or semi-automatically analyzed duringprotocol execution. These subsets would be selected to include data ofeffective power to provide a protocol that achieves a desired trade-offin ease of administration and the statistical validity of result.

Traditional statistical techniques useful in the data analysis stepsinclude regression, multivariate analysis, and principle componentanalysis (PCA). Those skilled in the art will be familiar with thesemathematical approaches. They are shown applied in this art in U.S. Pat.No. 6,056,671, Marmer; and Quantitative assessment of the controlcapability of the trunk muscles during oscillatory bending motion undera new experimental protocol, Kim, Parnianpour and Marras, ClinicalBiomechanics vol. 11, no. 7, 385-391, 1996. Both references are herebyincorporated herein by reference in their entireties.

In many cases, the powerful, non-linear techniques of data miningincluding training artificial neural nets (ANN), self-organizing maps(SOM), machine learning classifier trees, and fuzzy decision trees arecomprised in the data analysis. Those skilled in the art will befamiliar with these computational approaches. They are shown applied inthis art in US Published Patent Application 2005/0234309, Klapper, inU.S. Pat. No. 5,413,116, Radke et. al., and in U.S. Pat. No. 6,248,063,Barnhill. All three of these references are hereby incorporated hereinby reference in their entireties.

In many implementations consistent with these teachings the initial datais from a large number of subjects with known attributes relative to thephysical domain of interest. In other cases, for example with the use of“unsupervised” ANN or clustering techniques, there may not be subjectsof known, quantified capabilities. For some protocol creations it may beadvisable to have subjects of a known condition as well as “normals”. Inother cases one might have a subject population that is selected fromsubjects all suffering from a common condition but in varying degrees.In protocols for tracking changes in an individual, they are analyzed inlight of their own performance at various stages of recovery,deterioration, learning, or circumstances. FIGS. 3 and 4 show flowcharts of steps for creating an executable protocol.

FIGS. 3 and 4 together illustrate one example process for producing aprotocol for distinguishing between individuals with back pain andindividuals feigning back pain. The first step is determining orreceiving a physical motion domain of interest S101. In this case backpain self-reporting veracity is the subject. In the next step a widerange of motions and measurements of those motions is postulated S102 asrelevant to making the desired distinction. In the case of back pain,the rate and extent of spine movement is thought to be highly relevant.A Lumbar Motion Monitor that can measure position, velocity, andacceleration of the spine is selected to provide the raw data. TheLumbar Motion Monitor measures in the sagittal, lateral, and twistingplanes. A set of subjects with an appropriately wide range of backproblems is recruited and selected S103.

In creating the protocol, each subject selected goes through the sameset of steps. In FIG. 3 this is expressed by initiating a FOR loop S104.The next steps involve instrumenting the subject at hand S105 accordingto the previously determined instrumentation and directing the subjectto perform the previously determined motions S106. While the subject isperforming, those movements', raw data associated with those movementsis collected from the instrumentation and stored in a computer readablemedia S107. The end of the FOR loop for that subject is reached and ifthere are untested subjects S108 it is decided to return control to thetop of that loop S104.

When it is determined that the last subject has been tested S108 thecollected and stored data is packaged for analysis S109. This packagingmight involve adding non-motion information, providing a copy of thedata, or an address within a computer readable media to locate it. Morefrequently it will involve preprocessing the information to filter outnoise and non-meaningful data. It also might involve norming, usingprincipal component analysis for simplification of further datamanipulation.

The sequence continues, as shown in FIG. 4 by taking the packaged dataas a starting point S111. In this case the next step is to analyze thedata using linear regression for variables in the packaged data havinghigh correlation to the pre-known state of the individuals S112. Inother, protocols consistent with these teachings, many other forms ofanalysis can be used. Data mining techniques like cluster analysis aswell as artificial intelligence techniques including artificial neuralnetworks might be used in this step.

In the case of a traditional statistical method such as linearregression, the various measured parameters will each have a correlationcoefficient or other statistical confidence measure. The next step ranksthe various parameters by that statistical quantity S113 with the mostpredictive first. In a version using cluster analysis, the parameterlist might include a list of various subsets of the total, each subsetin order by its power. Starting with the first parameter S114 theprogram module loops through the parameters from top down adding themS115 to the list being compiled of candidate measurement for theprotocol being created. As each new parameter is added to the candidatelist, the predictive power of the items on the list, taken together, istested against the received packaged data S116. In FIG. 4 it is seenthat this loop is ended when the candidate parameter list reaches alevel that is deemed of a high enough significance S117 for the purposesof the protocol being created. In fact many processes consistent withthe teachings herein will also assign a rating representing thepracticality of making each type of measurement. This can allowoptimization for foolproof-ness to administer or time to administer, forexample. Therefore the cutoff as to significant-enough S117 could be setconservatively in order to allow for some “candidate” parameters to berejected for use in the finally produced protocol due to protocolconsiderations.

After the list of parameters is established, a detailed plan to makethose measurements efficiently using cost-effective instrumentation iscreated S118 which constitutes the basis of a protocol for; in thiscase, assessing the presence of back pain regardless of a subject'sself-reporting.

During Protocol Execution Example Data Collection Steps

FIG. 5 shows a flowchart of the data collection steps of an exampleexecutable protocol. Instrumentation that might be used in a tennisapplication is illustrated in FIG. 7 and further discussed herein.

Example Data Analysis Steps Performed by a Data Analysis Computer System

-   1) Accepting movement and force information from pre-processing    system.-   2) Doing at least one of: (a) applying predetermined decision    tree (b) feeding data to trained machine learning data structure to    determine classification or rating (c) applying linear or non-linear    model.-   3) Based on step 2 determine closet match under predetermined rules    and set as a rating.-   4) Outputting rating.    A flow chart illustrating an example of the steps involved in data    analysis during protocol execution is seen in FIG. 6.

Data Analysis Computational Techniques—for Protocol Execution

The data produced while testing an unknown subject may be firstpre-processed to extract pre-determined features. The data may benormalized in one or more dimensions. A rating or categorization may beassigned by linear calculation, by following a classification tree, orby providing data to a trained learning machine.

FIGS. 5 and 6 represent flowcharts of executing a predefined protocolfor assigning an “unknown” subject to one of multiple pre-identifiedclusters. FIG. 5 covers activities directly related to testing theindividual while FIG. 6 relates to steps taken to analyze theinformation from the tests.

The first step of FIG. 5 is receiving S120 the given subject. The nextstep is to instrument that subject S121 for motion measurements asdictated by the specific protocol being performed (back pain, tennisswing, gait, etc). A prescribed series of motions is performed S122 bythe subject while the motion data is being electronically recorded S123on suitable machine-readable media. Preprocessing steps that might beperformed at the time and place of the test include filtering,normalizing, and extracting biomechanical information S124 from therecorded raw data transforming it into a reduced and more meaningfulstate. Presuming that the analysis is done separately from theinformation collection, the information is then suitably packaged S125for analysis. As shown, the analysis is optionally performed at adifferent location on a different computer system.

The information transformation and analysis of that data is shown inFIG. 6. After the data is accepted S131, the clustering criteria to beapplied to that data is accepted S132. In this example, the accepted,preprocessed data is further processed S133 to extract salient featuresspecified by the clustering criteria to use as inputs for cluster matchdetermination S134. That cluster-matching step involves acomputer-implemented, mathematical comparison of the salient features ofthe accepted data to pre-defined, denoted, clusters of parametersspecified in the accepted cluster criteria.

If the transformed information representing the subject's motions isfound to conform to a denoted cluster with a predetermined degree ofstatistical acceptability S135, the denotation of that match is outputS138. In the case that the measured motion data of the subject does notalign with any predetermined cluster to an effective degree, the outputis that no classification is clearly indicated S136 by the data.

System

An example system for executing a protocol consistent with theprinciples taught herein is shown in FIG. 7. A person 1 is instrumentedin a minimal fashion with accelerometers 20 in three chosen locations.The accelerometers are coupled via cables 21 to a belt-mountedcontroller 22. The controller communicates by Bluetooth compliantwireless signals with a local data capturing and pre-processing computer24 for recording, initial filtering, normalization and formatting. Inone respect, the data capturing and pre-processing computer followsinstruction to act as an intelligent electronic recorder. In turn thatsystem communicates with a remotely located analysis and rating system25. An alternate example system can comprise, as its hardware portion, ahome console gaming system such as a Wii with motion and force sensorinputs. Portions of a system can comprise one or more devices like aniTouch or iPhone, which can have accelerometers, GPS, and computationalcapabilities.

System Variations

Other system versions might instrument the human with an upper body,lower body or full body suit such as suits in the MVN brand product lineoffered by Xsens Technologies B.V. and illustrated in FIG. 2A. For someapplications the motion or force sensors of a home game system's inputdevice or those in a portable device such as an iTouch may be adequateto make the measurements.

The data preparation system and data analysis and rating system might beremote, might be co-located, or might be implemented on a singlecomputer server. The computational devices used to carry out the methodcould be a personal computer. In some versions, the system might promptthe subject to perform the limited set of motions. This might be via atext display, by spoken output, or preferably by a video demonstration.In addition, a particular system version could provide a warning that asequence of motions was not performed as per-protocol and inform thesubject or clinician. In some cases, the subject and the computerperforming the analysis might not be co-located. At a central facilityfor data analysis computation, trained computer learning systems, andexpertise may serve many protocol execution sites. A camera 23 might beused to capture still or video images of the protocol execution to bestored along with the motion data for future verification of correctprotocol administration.

Some embodiments will be broken down into foolproof steps, figurativelya “paint-by-numbers” execution protocol. At another time and locationmore trained personnel can carry out other steps of data analyzing andassignment of discrete classification or of a rating. Below is a pseudocode “flowchart” of an example protocol execution for rating a tennisswing.

Pseudo Code of Protocol Execution START Instrument Subject, in apredetermined manner for position, motion and force sensing; Operativelycouple sensing equipment to data capture computer; Initiate informationcapturing by sensors and data capture computer; Direct subject toperform predetermined tennis swing motion sequences while capturingmotion and force and position information; Capture photographicinformation of subject while subject is performing predetermined motionsequences; Command data analysis computer to pre-process capturedinformation and format and package for analysis; Operatively communicateformatted and packaged information from data capture computer to dataanalysis computer; Command data analysis computer to statisticallycompare information communicated from data capture computer to adatabase of a set of predetermined quantitative criteria of movementperformance; Is there statistically significant agreement of packagedinformation and predetermined criteria? TRUE: Output numerical ratingassociated with criteria match FALSE: Output: “No reliable match found” END

The various illustrative program modules and steps described inconnection with the embodiments disclosed herein may be implemented aselectronic hardware, computer software, or combinations of both. Thevarious illustrative program modules and steps have been describedgenerally in terms of their functionality. Whether the functionality isimplemented as hardware or software depends in part upon the hardwareconstraints imposed on the system. Hardware and software may beinterchangeable depending on such constraints. As examples, the variousillustrative program modules and steps described in connection with theembodiments disclosed herein may be implemented or performed with anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA) or other programmable logic device, discrete gate ortransistor logic, discrete hardware components, a conventionalprogrammable software module and a processor, or any combination thereofdesigned to perform the functions described herein. The processor may bea microprocessor, CPU, controller, microcontroller, programmable logicdevice, array of logic elements, or state machine. The software modulemay reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROMmemory, hard disk, a removable disk, a CD, DVD or any other form ofstorage medium known in the art. An example processor may be coupled tothe storage medium so as to read information from, and write informationto, the storage medium. In the alternative, the storage medium may beintegral to the processor.

In further embodiments, those skilled in the art will appreciate thatthe foregoing methods can be implemented by the execution of a programembodied on a computer readable medium either tangible or intangible.The medium may comprise, for example, RAM accessible by, or residingwithin the device. Whether contained in RAM, a diskette, or othersecondary storage media, the program modules may be stored on a varietyof machine-readable data storage media such as a conventional “harddrive”, magnetic tape, electronic read-only memory (e.g., ROM orEEPROM), flash memory, an optical storage device (e.g., CD, DVD, digitaloptical tape), or other suitable data storage media. Those skilled inthe art will recognize that the embodiments described herein are readilyproducible using known techniques, materials and equipment. Thisteaching is presented for purposes of illustration and description butis not intended to be exhaustive or limiting to the forms disclosed.Many modifications and variations will be apparent to those of ordinaryskill in the art. The claims below, in contrast, set out its metes andbounds. In the claims, the words “a” and “an” are to be taken to mean“at least one” even if some claim wording explicitly calls for “at leastone” or “one or more”. In addition any predetermined value, criteria, orrule in the claims may be predetermined at any time up to the time it isrequired for effective operation unless explicitly stated otherwise.

It is claimed:
 1. A protocol for ascribing a discrete rating to anaspect of human physical performance of a subject comprising:instrumenting the subject to measure, by electronic systems, apredetermined set of motion parameters; directing the subject through apredetermined sequence of motions; collecting real-time datarepresentative, at least, of subject motions; analyzing, according topredetermined criteria, at least a portion of the collected real-timedata with a computer system programmed for biomechanical datacomparison; determining of a discrete rating based upon the analyzing;where the predetermined motion parameters, predetermined set of motionsand predetermined criteria were previously derived by a statisticalanalysis of measurements of a proper subset of the motion parameters ona proper subset of the motions on a plurality of individuals of knownability in the aspect of human physical performance; the derivationproducing the respective predetermined subsets and criteria to comprisean efficient protocol with a desired degree of accuracy andrepeatability.
 2. The protocol of claim 1 where the discrete ratingcomprises an overall figure of merit.
 3. The protocol of claim 1 wherethe discrete rating comprises a numeric expression.
 4. The protocol ofclaim 1 where the instrumenting comprises GFR.
 5. The protocol of claim1 where the instrumenting comprises accelerometers.
 6. The protocol ofclaim 1 where the instrumenting and analyzing are performed at distinctlocations and times.
 7. A protocol for ascribing a discrete rating to anaspect of human physical performance of a subject comprising: analyzingcollected real-time data with a computer system programmed forbiomechanical data comparison, the computer system having a processorand a memory; the analysis according to predetermined criteria;determining a discrete rating based on the analyzing; where thecollected real-time data is representative, at least, of subject'smotions determined by instrumenting the subject to measure apredetermined set of motion parameters by electronic systems while thesubject is being directed through a predetermined sequence of specificmotions; where the predetermined motion parameters, predetermined set ofmotions and predetermined criteria were previously derived by astatistical analysis of measurements of a proper subset of the motionparameters on a proper subset of the motions on a plurality ofindividuals of known ability in the aspect of human physicalperformance; the derivation producing the respective predetermined setsand criteria to comprise an efficient protocol with a desired degree ofaccuracy and repeatability.
 8. The protocol of claim 7 where thediscrete rating comprises an overall figure of merit.
 9. The protocol ofclaim 7 where the discrete rating comprises a numeric expression. 10.The protocol of claim 7 where the discrete rating comprises an estimateof the probability that the subject is feigning a degree of ability. 11.The protocol of claim 7 where the statistical analysis comprisesclustering analysis.
 12. The protocol of claim 7 where the statisticalanalysis comprises regression.
 13. The protocol of claim 7 where theanalysis of real-time data comprises includes step of pre-processing byprincipal component analysis.
 14. A non-transient computer readablemedium containing instructions executable by a computer system having aprocessor and memory and input/output facilities, the instructionsdirecting the action of: analyzing biomechanical data representative ofselected motions of an individual according to a predetermined criteria,the biomechanical data representative of, at least, a predetermined setof motion parameters taken during a directed, predetermined set ofmotions of the individual; the analyzing resulting in assigning adiscrete designation of ability in a specific domain of human physicalperformance; where the predetermined motion parameters, predeterminedset of motions and predetermined criteria were previously derived by astatistical analysis of measurements of a proper subset of the motionparameters on a proper subset of the motions on a plurality ofindividuals of known ability in the domain of human physicalperformance; the derivation producing the respective predeterminedsubsets and criteria.
 15. The non-transient computer readable medium ofclaim 14 where the discrete rating comprises an overall figure of merit.16. The non-transient computer readable medium of claim 14 where thediscrete rating comprises a numeric expression.
 17. The non-transientcomputer readable medium of claim 14 where the discrete rating comprisesan estimate of the probability the individual is feigning a degree ofability.
 18. The non-transient computer readable medium of claim 14where the analysis of biomechanical data comprises pre-processing byprincipal component analysis.
 19. The non-transient computer readablemedium of claim 14 where the executable instructions further compriseinstructions for collecting real-time data representative ofbiomechanical data to be analyzed.